Feature extraction plays an important role in the field of fault diagnosis of analog circuits. How to effectively extract fault features is crucial to diagnostic accuracy. The components tolerance and circuit nonlinearities of analog circuits can cause some part overlapping of primal signal among different component faults in time domain and frequency domain. Currently, the existing method aims at wavelet features, statistical property features, conventional frequency features and conventional time-domain features. There is no decoupling ability for the feature extraction methods mentioned above. To solve the problem, a new fault features extraction method is proposed. The diagnostic results are compared with those from other methods. Firstly, it is proposed to use the statistical property features of transformed signals by the fractional Fourier transform in the optimal fractional order domain as fault features, such as range, mean, standard deviation, skewness, kurtosis, entropy, median, the third central moment, and centroid. And then, KPCA is used to reduce the dimensionality of candidate features so as to obtain the optimal features. Next, normalization is applied to rescale input features. Finally, extracted features are trained by SVM to diagnose faulty components in analog circuits. The simulation results show that compared with traditional methods, the proposed method is quite efficient to improve diagnostic accuracy.

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The flower pollination algorithm (FPA) is a novel optimization technique derived from the pollination behavior of flowers. However, the shortcomings of the FPA, such as a tendency towards premature convergence and poor exploitation ability, confine its application in engineering problems. To further strengthen FPA optimization performance, an orthogonal learning (OL) strategy based on orthogonal experiment design (OED) is embedded into the local pollination operator. OED can predict the optimal factor level combination by constructing a smaller but representative test set based on an orthogonal array. Using this characteristic of OED, the OL strategy can extract a promising solution from various sources of experience information, which leads the population to a potentially reasonable search direction. Moreover, the catfish effect mechanism is introduced to focus on the worst individuals during the iteration process. This mechanism explores new valuable information and maintains superior population diversity. The experimental results on benchmark functions show that our proposed algorithm significantly enhances the performance of the basic FPA and offers stronger competitiveness than several state-of-the-art algorithms.

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